非线性工业过程控制系统的迭代学习控制与收敛性分析

The Iterative Learning Control and Convergence Analysis for Nonlinear Industrial Process Control Systems

基于工业过程稳态优化中递阶控制结构和线性工业过程控制系统中的迭代学习控制规律 ,本文对饱和非线性工业过程控制系统和变增益非线性工业过程控制系统施行迭代学习控制 ,分别给出加权PD 型闭环迭代学习控制算法和加权幂型开闭环迭代学习控制算法 ,提出了期望目标轨线的 δ 可达性和迭代学习算法的ε 收敛性的概念 .利用Bellman Gronwall不等式和λ 范数理论 ,论证了算法的收敛性 .数字仿真表明 。

Based on hierarchical control structure in steady state optimization of industrial processes and iterative learning control law for linear industrial process control systems, the iterative learning control is applied to saturated nonlinear industrial control systems and nonlinear industrial control systems with changing gains, the weighted PD type closed loop iterative learning control algorithm and weighted power type open closed loop iterative learning algorithm are discussed respectively. The definitions of δ reachability of objective trajectory and ε convergence of the iterative learning control algorithm are suggested. By means of Bellman Gronwall inequality and λ norm theory, the convergence of the algorithms is also proved. The numerical simulation shows that the iterative learning control can remarkably improve the dynamic performance of industrial control systems in steady state optimizing.